Enhanced local support vector machine with fast cross-validation capability

Yu Ann Chen, Pau Choo Chung

研究成果: Conference contribution

摘要

Local SVM is a lazy learner combining k-nearest neighbor search and support vector machine classifier. We propose an improved implementation of local SVM which utilizes tree structure for efficient nearest neighbor search and a method to avoid unnecessary SVM training in areas far from decision boundary. The proposed lazy learner has great advantage on cross-validation efficiency while maintaining comparable accuracy to traditional SVM. The proposed method also enables us to conduct leave-one-out cross-validation which is previously considered too time-consuming to be practical on large dataset.

原文English
主出版物標題Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
編輯William Cheng-Chung Chu, Stephen Jenn-Hwa Yang, Han-Chieh Chao
發行者IOS Press
頁面491-500
頁數10
ISBN(電子)9781614994831
DOIs
出版狀態Published - 2015 一月 1
事件International Computer Symposium, ICS 2014 - Taichung, Taiwan
持續時間: 2014 十二月 122014 十二月 14

出版系列

名字Frontiers in Artificial Intelligence and Applications
274
ISSN(列印)0922-6389

Other

OtherInternational Computer Symposium, ICS 2014
國家Taiwan
城市Taichung
期間14-12-1214-12-14

    指紋

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

引用此

Chen, Y. A., & Chung, P. C. (2015). Enhanced local support vector machine with fast cross-validation capability. 於 W. C-C. Chu, S. J-H. Yang, & H-C. Chao (編輯), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014 (頁 491-500). (Frontiers in Artificial Intelligence and Applications; 卷 274). IOS Press. https://doi.org/10.3233/978-1-61499-484-8-491